Fixed segmented lattice planning for a mobile automation apparatus

ABSTRACT

Fixed segmented lattice planning for a mobile automation apparatus is provided. A mobile automation apparatus is provisioned with a plurality of segments for a plurality of paths through an environment, each of the plurality of segments being fixed in a reference frame, the plurality of segments arranged in a lattice configuration, with adjacent segments defining fixed nodes in the lattice configuration. The apparatus navigates through the environment on a segment-by-segment basis, storing control inputs and error signals for each segment and then later, when again navigating a segment using stored control inputs and error signals to generate current control inputs, along with current error signals, and storing the current control inputs and the current error signals. Indeed, each time the apparatus navigates a segment in the lattice configuration, the control inputs and the error signals are updated to refine navigation through the environment at each navigation through a segment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to U.S. Provisional Application No.62/492,670 entitled “Product Status Detection System,” filed on May 1,2017, by Perrella et al., which is incorporated herein by reference inits entirety.

BACKGROUND OF THE INVENTION

Mobile automation apparatuses are increasingly being used in retailenvironments to perform pre-assigned tasks, such as inventory tracking.To perform these tasks, some mobile automation apparatuses rely onvarious sensors to navigate within the retail environment. As the mobileautomation apparatus navigates the environment, tight positionalconstraints should be satisfied for the mobile automation apparatus tocapture high quality imaging data. As such, the mobile automationapparatus generally attempts to follow a single, static predefined paththat is believed to be optimal for its environment. However, due to thehighly varying and dynamic nature of a retail environment, a mobileautomation apparatus often deviates from the single predefined path toavoid obstacles in arbitrary locations. When such deviations occur,under a conventional approach, the deviated path cannot use the learnedcontrol inputs, nor will the mobile automation apparatus attempt tolearn control inputs along the deviation. Instead, the mobile automationapparatus will simply attempt to navigate back to the single predefinedoptimal path.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a block diagram of a mobile automation system in accordancewith some embodiments.

FIG. 2 is a side view of a mobile automation apparatus in accordancewith some embodiments.

FIG. 3 is a block diagram of the mobile automation apparatus of FIG. 2in accordance with some embodiments.

FIG. 4 is a block diagram of a control application in accordance withsome embodiments.

FIG. 5 is a flowchart of a method for navigating a mobile automationapparatus using a fixed segmented lattice planning in accordance withsome embodiments.

FIG. 6 is a top view of the mobile automation apparatus of FIG. 2 in anenvironment, along with a depiction of fixed segments and fixed nodesused for navigation in accordance with some embodiments.

FIG. 7 depicts initial path generation using a subset of the fixedsegments of FIG. 6 in accordance with some embodiments.

FIG. 8 depicts branch generation along the generated path of FIG. 7 inaccordance with some embodiments.

FIG. 9 schematically depicts branch generation at the fixed segments ofFIG. 6 in accordance with some embodiments.

FIG. 10 depicts an updated path and an actual path of the mobileautomation apparatus navigating using the fixed segments in accordancewith some embodiments.

FIG. 11 is a flowchart of a method for navigating a mobile automationapparatus using a fixed segmented lattice planning in accordance withsome embodiments.

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION OF THE INVENTION

An aspect of the specification provides a mobile automation apparatuscomprising: a memory storing a plurality of segments for a plurality ofpaths through an environment, each of the plurality of segments beingfixed in a reference frame, and extending between fixed nodes arrangedin a lattice configuration; a navigation module having at least onemotor configured to move the mobile automation apparatus in theenvironment; and a navigation controller configured to: as navigationoccurs a first time along each of respective segments using thenavigation module, store, in the memory, control inputs and errorsignals for the navigation module in association with each of therespective segments; and, as navigation occurs at least a second timealong a given segment: generate current control inputs for the givensegment based on current error signals and the control inputs and theerror signals stored in the memory for the given segment; and store thecurrent control inputs and the current error signals in the memory inassociation with the given segment.

In some implementations, the mobile automation apparatus furthercomprises one or more obstacle avoidance sensors, wherein the navigationcontroller is further configured to: navigate along a first path usingthe control inputs and the error signals stored in the memory inassociation with each of the respective segments of the first path; whenan obstacle is detected in the first path, using the one or moreobstacle avoidance sensors: control the navigation module to navigatefrom a fixed node in the first path to a respective fixed node in asecond path to avoid the obstacle, using a branch from the fixed node inthe first path to the respective fixed node in the second path; andstore, in the memory, the control inputs and the error signals for thenavigation module in association with each of the respective segmentsfor the second path; and, when the one or more obstacle avoidancesensors indicate that the mobile automation apparatus is past theobstacle: control the navigation module to navigate from a furtherrespective fixed node in the second path to a further fixed node in thefirst path, using another branch from the further respective fixed nodein the second path to the further fixed node in the first path; andstore, in the memory, the control inputs and the error signals for thenavigation module in association with each of the respective segmentsfor the second path and the first path. In some of theseimplementations, the second path is one or more of: adjacent to thefirst path; about parallel to the first path; and within a givenconstraint boundary.

In some implementations, the mobile automation apparatus furthercomprises a communication interface configured to receive a destinationposition in the environment, and wherein the navigation controller isfurther configured to: generate a path to the destination position usinga subset of the plurality of segments. In some of these implementations,the navigation controller is further configured to: generate branches ateach fixed node in the path, at least a portion of the branchescomprising a branch from a fixed node in a first path to a respectivefixed node in an adjacent path used to avoid obstacles in the path. Insome of these implementations, the navigation controller is furtherconfigured to: generate branches at each fixed node in the path when thepath is generated. In some of these implementations, the navigationcontroller is further configured to: generate branches at each fixednode in the path, each of the branches associated with a set ofrespective control inputs used by the navigation module to navigate froma respective fixed node in the path to one or more fixed nodes inadjacent paths.

In some implementations, the navigation controller is further configuredto: determine the control inputs and the error signals for thenavigation module using a learning controller; and generate the currentcontrol inputs using the learning controller.

In some implementations, the navigation controller is further configuredto: determine when one or more constraints for navigating the givensegment has been violated; and generate the current control inputs thatkeep the one or more constraints within respective threshold values.

In some implementations, the navigation controller is further configuredto change a current path when one or more constraints for navigating therespective segments have been violated by navigating along an updatedpath that meets the one or more constraints.

In some implementations, the reference frame is associated with theenvironment.

In some implementations, the lattice configuration comprises the fixednodes arranged in a periodic pattern in at least two orthogonaldirections according to a given resolution.

In some implementations, the lattice configuration has a firstresolution in a first region of the environment and a second resolutionin a second region of the environment.

In some of these implementations, a resolution of the latticeconfiguration is dynamic, and the navigation controller is furtherconfigured to: change a resolution of the lattice configuration in aregion to an updated resolution, and generate new control inputs anddetermine associated new error signals for each of updated segments inthe lattice configuration at the updated resolution.

Another aspect of the specification provides a method comprising: at amobile automation apparatus comprising: a memory storing a plurality ofsegments for a plurality of paths through an environment, each of theplurality of segments being fixed in a reference frame, and extendingbetween fixed nodes arranged in a lattice configuration; a navigationmodule having at least one motor configured to move the mobileautomation apparatus in the environment, as navigation occurs a firsttime along each of respective segments using the navigation module,storing, using the navigation controller, in the memory, control inputsand error signals for the navigation module in association with each ofthe respective segments; and, as navigation occurs at least a secondtime along a given segment: generating, using the navigation controller,current control inputs for the given segment based on current errorsignals and the control inputs and the error signals stored in thememory for the given segment; and storing, using the navigationcontroller, the current control inputs and the current error signals inthe memory in association with the given segment.

In some implementations, the mobile automation apparatus furthercomprises one or more obstacle avoidance sensors, and the method furthercomprises: navigating, using the navigation controller, along a firstpath using the control inputs and the error signals stored in the memoryin association with each of the respective segments of the first path;when an obstacle is detected in the first path, using the one or moreobstacle avoidance sensors: controlling, using the navigationcontroller, the navigation module to navigate from a fixed node in thefirst path to a respective fixed node in a second path to avoid theobstacle, using a branch from the fixed node in the first path to therespective fixed node in the second path; and storing, using thenavigation controller, in the memory, the control inputs and the errorsignals for the navigation module in association with each of therespective segments for the second path; and, when the one or moreobstacle avoidance sensors indicate that the mobile automation apparatusis past the obstacle: controlling, using the navigation controller, thenavigation module to navigate from a further respective fixed node inthe second path to a further fixed node in the first path, using anotherbranch from the further respective fixed node in the second path to thefurther fixed node in the first path; and storing, using the navigationcontroller, in the memory, the control inputs and the error signals forthe navigation module in association with each of the respectivesegments for the second path and the first path.

In some implementations, the mobile automation apparatus furthercomprises a communication interface configured to receive a destinationposition in the environment, and wherein the method further comprisesgenerating, using the navigation controller, a path to the destinationposition using a subset of the plurality of segments.

In some implementations, the method further comprises: determining thecontrol inputs and the error signals for the navigation module using alearning controller; and generating the current control inputs using thelearning controller.

In some implementations, the method further comprises: determining whenone or more constraints for navigating the given segment has beenviolated; and generating the current control inputs that keep the one ormore constraints within respective threshold values.

Another aspect of the specification provides a non-transitorycomputer-readable medium storing a computer program, wherein executionof the computer program is for: at a mobile automation apparatuscomprising: a memory storing a plurality of segments for a plurality ofpaths through an environment, each of the plurality of segments beingfixed in a reference frame, and extending between fixed nodes arrangedin a lattice configuration; a navigation module having at least onemotor configured to move the mobile automation apparatus in theenvironment, as navigation occurs a first time along each of respectivesegments using the navigation module, storing, using the navigationcontroller, in the memory, control inputs and error signals for thenavigation module in association with each of the respective segments;and, as navigation occurs at least a second time along a given segment:generating, using the navigation controller, current control inputs forthe given segment based on current error signals and the control inputsand the error signals stored in the memory for the given segment; andstoring, using the navigation controller, the current control inputs andthe current error signals in the memory in association with the givensegment.

FIG. 1 depicts a mobile automation system 100 in accordance with theteachings of this disclosure. The system 100 includes a server 101 incommunication with at least one mobile automation apparatus 103 (alsoreferred to herein simply as the apparatus 103) and at least one mobiledevice 105 via communication links 107, illustrated in the presentexample as including wireless links. The system 100 is deployed, in theillustrated example, in a retail environment including a plurality ofmodules 110 of shelves each supporting a plurality of products 112. Inthe illustrated embodiment, the apparatus 103 is deployed within theretail environment, such as in a store, and at least periodicallycommunicates with the server 101 (via the link 107) as it navigates,autonomously or at least partially autonomously, the length of at leasta portion of the shelf modules 110. The apparatus 103 is equipped with aplurality of data capture and/or navigational sensors, such as imagesensors (e.g. one or more digital cameras) and depth sensors (e.g. oneor more Light Detection And Ranging (lidar) sensors), and is furtherconfigured to employ at least some of the sensors to capture shelf data.In the present example, the apparatus 103 is configured to capture aseries of digital images of the modules 110, as well as a series ofdepth measurements, each describing the distance and direction betweenthe apparatus 103 and a point associated with the module 110, such asthe shelf module 110 itself or products disposed thereon.

The server 101 includes a controller 120 that is configured to at leastperiodically communicate with the mobile automation apparatus 103, whichnavigates the environment and captures data, to obtain the captured datavia the communications interface 124 and store the captured data in arepository 132 of the memory 122. The server 101 is specificallyconfigured to perform various post-processing operations on the captureddata, and to detect the status of the products 112 on the shelf modules110. When certain status indicators are detected, the server 101 is alsoconfigured to transmit status notifications to the mobile device 105.The controller 120 is interconnected with a non-transitory computerreadable storage medium, such as a memory 122. The memory 122 includes asuitable combination of volatile (e.g. Random Access Memory or RAM) andnon-volatile memory (e.g. read only memory or ROM, Electrically ErasableProgrammable Read Only Memory or EEPROM, flash memory). The controller120 and the memory 122 each comprise one or more integrated circuits.The controller 120, for example, includes a special purpose controllerspecifically designed to manage the mobile automation apparatus 103 andhaving one or more of central processing units (CPUs) and graphicsprocessing units (GPUs) and/or one or more of field-programmable gatearrays (FPGAs) and/or application-specific integrated circuits (ASICs).In an embodiment, a specially designed integrated circuit, such as aField Programmable Gate Array (FPGA), is designed to perform thenavigational and/or image processing functionality discussed herein,either alternatively or in addition to the controller 120 and memory122. As further discussed below, the mobile automation apparatus 103also includes one or more controllers or processors and/or FPGAs 320(FIG. 3), in communication with the controller 120, specificallydesigned and configured to control navigational and/or data captureaspects of the apparatus 103. As those of skill in the art will realize,in various embodiments, the controller 320 of the mobile automationapparatus 103 performs some or all of the functionality described hereinwith respect to the controller 120 of the server 101 and vice versa.

The server 101 also includes a communications interface 124interconnected with the controller 120. The communications interface 124includes suitable hardware (e.g. transmitters, receivers, networkinterface controllers and the like) allowing the server 101 tocommunicate with a plurality of computing devices—particularly theapparatus 103 and the mobile device 105—via the links 107. The links 107may be direct links, or links that traverse one or more networks,including both local and wide-area networks. The specific components ofthe communications interface 124 are selected based on the type ofnetwork or other links that the server 101 is required to communicateover. In the present example, a wireless local-area network isimplemented within the retail environment via the deployment of one ormore wireless access points. The links 107 therefore include bothwireless links between the apparatus 103 and the mobile device 105 andthe above-mentioned access points, and a wired link (e.g. anEthernet-based link) between the server 101 and the access point.

The memory 122 stores a plurality of applications, each including aplurality of computer readable instructions executable by the controller120. The execution of the above-mentioned instructions by the controller120 configures the server 101 to perform various actions discussedherein. The applications stored in the memory 122 include a controlapplication 128, which may also be implemented as a suite of logicallydistinct applications. In general, via execution of the controlapplication 128 or subcomponents thereof, the controller 120 isconfigured to implement various functionality. The controller 120, asconfigured via the execution of the control application 128, is alsoreferred to herein as the controller 120. As will now be apparent, someor all of the functionality implemented by the controller 120 describedbelow may alternatively or in addition be performed by preconfiguredspecial purpose hardware elements, such as one or more FPGAs orApplication-Specific Integrated Circuits (ASICs).

For example, in some embodiments, the server 101 is configured via theexecution of the control application 128 by the controller 120, toprocess image and depth data captured by the apparatus 103 to identifyportions of the captured data depicting a back of a shelf module 110,and to detect gaps between the products 112 based on those identifiedportions. In some embodiments navigation of the mobile automationapparatus 103 is fully autonomous, while in other embodiments the server101 facilitates navigation of the mobile automation apparatus 103 byproviding a map and/or paths and/or path segments and/or navigation dataand/or navigation instructions to the apparatus 103 to help theapparatus 103 navigate among the modules 110.

Attention is next directed to FIG. 2 and FIG. 3 which respectivelydepict: a schematic side perspective view of the apparatus 103; and aschematic block diagram of the apparatus 103.

With reference to FIG. 2, the apparatus 103 is being operated adjacentat least one of the modules 110. The apparatus 103 generally comprises:a base 210 configured to move on wheels 212 (e.g. on a floor 213 of theenvironment), and the like; and a mast 214 and/or support structureextending in an upward direction (e.g. vertically) from the base 210.However, in other embodiments, the base 210 moves on devices other thanwheels, for example casters, tracks and the like. In yet furtherimplementations, the mast 214 and the base 210 form a housing. In yetfurther implementations, a housing of the apparatus 103 has a shapedifferent than the depicted mast/base arrangement.

The apparatus 103 further includes one or more obstacle avoidancesensors 230 configured to detect obstacles in the environment, forexample as the apparatus 103 is navigating through the environment. Theone or more obstacle avoidance sensors 230 is also configured to assistthe apparatus 103 with navigating the environment, for example byacquiring respective data used by the apparatus 103 to navigate a paththrough the environment. The one or more obstacle avoidance sensors 230comprises one or more of: image devices (including image sensors, depthcameras, structured light cameras, time-of-flight cameras), LiDAR (LightDetection and Ranging) sensors, ultrasound sensors, and the like,arranged at various positions on the base 210 and the mast 214 inpositions to detect obstacles at various heights and directions from theapparatus 103. In general, the one or more obstacle avoidance sensors230 detect obstacles around the apparatus 103 and provide data regardingthe location of the obstacles to a controller 320 (FIG. 3) of theapparatus 103; the controller 320 then navigates to avoid the obstacles.

While not depicted, the base 210 and the mast 214 are provisioned withother various navigation sensors for navigating in the environment inwhich the modules 110 are located and/or one or more data acquisitionsensors for capturing data associated with products, labels and the likeon shelves of the modules 110.

Referring now to FIG. 3, a schematic block diagram of further componentsof the apparatus 103 is depicted. In particular, the apparatus 103includes a navigation and data capture controller 320, interconnectedwith a non-transitory computer readable storage medium, such as a memory322. The memory 322 includes a suitable combination of volatile (e.g.Random Access Memory or RAM) and non-volatile memory (e.g. read onlymemory or ROM, Electrically Erasable Programmable Read Only Memory orEEPROM, flash memory). In general, the controller 320 and the memory 322each comprise one or more integrated circuits. The controller 320, forexample, includes a suitable combination of central processing units(CPUs), graphics processing units (GPUs), field-programmable gate arrays(FPGAs), and/or application-specific integrated circuits (ASICs)configured to implement the specific functionality of the apparatus 103.Further, as depicted, the memory 322 includes a repository 332 forstoring data, for example data collected by sensor(s) 230 and/or sensors330.

The apparatus 103 also includes a communications interface 324interconnected with the controller 320. The communications interface 324includes suitable hardware (e.g. transmitters, receivers, networkinterface controllers and the like) allowing the apparatus 103 tocommunicate with other devices—particularly the server 101 and,optionally, the mobile device 105—for example via the links 107, and thelike. The specific components of the communications interface 324 areselected based on the type of network or other links over which theapparatus 103 is required to communicate, including, but not limited to,the wireless local-area network of the retail environment of the system100.

The memory 322 stores a plurality of applications, each including aplurality of computer readable instructions executable by the controller320. The execution of the above-mentioned instructions by the controller320 configures the apparatus 103 to perform various actions discussedherein. The applications stored in the memory 322 include a controlapplication 328, which may also be implemented as a suite of logicallydistinct applications. In general, via execution of the controlapplication 328 or subcomponents thereof, the controller 320 isconfigured to implement various functionality. As will now be apparent,some or all of the functionality implemented by the controller 320described below may also be performed by specifically preconfiguredhardware elements (e.g. one or more ASICs) rather than by execution ofthe control application 328 by the controller 320.

As depicted, the memory 322 stores data 398 comprising: a plurality ofsegments for a plurality of paths through the environment, each of theplurality of segments being fixed in a reference frame, the plurality ofsegments arranged in a lattice, with adjacent segments defining fixednodes in the lattice. In some implementations, the data 398 is at leastpartially generated by the controller 320 as the apparatus 103 isnavigating the environment. In other implementations, the data 398 is atleast partially generated by the controller 320 as the apparatus 103 isnavigating the environment in a mapping mode that collects and storesnavigational data describing the environment (e.g., shelf module andpermanent obstacle locations) for future navigation sessions. In yetfurther implementations, the data 398 is at least partially provisionedat the memory 322, for example by server 101 using the links 107,presuming the server 101 is configured to generate the data 398 based ongeographic information about the environment (e.g. locations andphysical configuration of the modules 110 in the environment); in someof these implementations, the geographic information about theenvironment is at least partially acquired by the apparatus 103 (e.g. ina mapping mode and/or while navigating the environment) and transmittedto the server 101. Regardless, the data 398 generally defines a map ofthe environment and/or paths through the environment along which theapparatus 103 may navigate. As depicted, the data 398 is stored in therepository 332, however the data 398 is alternatively stored outside ofthe repository 332 and/or in association with the application 328.

The controller 320 is in further communication with the one or moreobstacle avoidance sensors 230, and with further navigation and/or dataacquisition sensors 330, including, but not limited to, LiDAR (LightDetection and Ranging) sensors, structured light cameras, proprioceptivesensors, as well as other navigation sensors and/or data acquisitionsensors.

The apparatus 103 further includes a navigation module 340 configured tomove the apparatus 103 in an environment, for example the environment ofthe modules 110, as based on the data 398. The navigation module 340comprises any suitable combination of motors, electric motors, steppermotors, and the like configured to drive and/or steer the wheels 212,and the like, of the apparatus 103.

In particular, the navigation module 340 is controlled according tocontrol inputs and further generates error signals and/or causes errorsignals to be generated. Such control inputs include data which causethe navigation module 340 to move between positions on a path in theenvironment, for example data which controls the wheels 212 to steer,brake, move forward, move backward, and the like, including, but notlimited to data to control each of the wheels 212 in a differentialmanner (e.g. each of the wheels 212 can be controlled according todifferent control inputs).

Error signals are generated at least when the navigation module 340implements the navigation and/or control of the wheels 212. However,error signals may be generated regardless of whether the navigationmodule 340 is implementing navigation; indeed, the error signals aregenerated while the apparatus 103 is operational. In someimplementation, such error signals generally comprise data indicative ofa navigation error and the like and hence can be alternatively referredto as error data. Regardless, the error signals are storable as data atthe memory 122, 322. The error signals include, but are not limited to,one or more of: error signals from navigation sensors, and the like,indicating whether the apparatus 103 is deviating from a path; errorsignals from proprioceptive sensors that monitor, for example, slippageby the wheels; error signals from the navigation module 340 thatindicate issues in implementing the control inputs; errors signalsindicating whether the apparatus 103 is violating any navigationconstraints, for example being outside an imaging range, violating anangle constraint and the like. Violating an angle constraint includesdetermining that the apparatus 103 is at an angle to a module 110 wherehigh-quality images cannot be acquired (e.g. images where labels and thelike on the shelves of the modules 110 cannot be imaged adequately forcomputer vision algorithms to extract data therefrom). Other types oferror signals will occur to those skilled in the art including, but notlimited to, error data that includes an obstacle indication associatedwith a given path segment, wheel slippage events, angle constraintviolations, as well as navigation instructions and/or actual navigationpositions that resulted in violations of positional constraints.

Hence, in general, the controller 320 is configured to control thenavigation module 340 to navigate the environment of the module 110using data from the one or more obstacle avoidance sensors 230 and/ornavigation sensors and/or other sensors.

While not depicted, the apparatus 103 is generally powered by a battery.

In the present example, the apparatus 103 is configured via theexecution of the control application 328 by the controller 320, tocontrol navigation module 340 to navigate the environment and avoidobstacles using fixed segmented lattice planning as described hereafter.

Turning now to FIG. 4, before describing the operation of theapplication 328 to control the navigation module 340, certain componentsof the application 328 will be described in greater detail. As will beapparent to those skilled in the art, in other examples the componentsof the application 328 may be separated into distinct applications, orcombined into other sets of components. Alternatively, or in addition,to improve navigation processing reliability and speed, at least some ofthe components of FIG. 4 are programmed directly into the navigation anddata capture controller 320, which may be an FPGA or an ASIC havingcircuit and memory configuration specifically designed to optimizenavigation path processing and capture of high volume of associatedsensor data. In such an embodiment, some or all of the controlapplication 328, discussed below, is an FPGA or an ASIC chip.

The control application 328 includes a learning controller 401. Inbrief, the learning controller 401 is configured to navigate theapparatus 103 along a path to a destination location while avoidingobstacles while learning how to best navigate the apparatus 103 alongsegments of the path. In some implementations, navigation occurs whilethe apparatus 103 is capturing and/or acquiring data associated withproducts, labels and the like on shelves of the modules 110 using thedata acquisition sensors. As the data acquisition sensors can requiretight operational data acquisition constraints (e.g. so that products,labels and the like on shelves of the modules 110 are within a depth offield of the data acquisition sensors, such as cameras, and the like),the learning controller 401 performs the navigation within predeterminedoperational constraints, as described below. However, techniquesdescribed herein are not dependent on the apparatus 103 capturing and/oracquiring data.

The learning controller 401 includes a path determiner component 418configured to determine a path through the environment based, forexample, on a destination location (e.g. received from the server 101using the links 107) and the data 398, where the navigation occurs on asegment-by-segment basis as described below.

The learning controller 401 further includes a control input monitor 419configured to monitor and store control input to the navigation module340 while the apparatus 103 is navigating a path through theenvironment. The control input to the navigation module 340 is stored indata 398 in association with respective segments at which the controlinput was acquired.

The learning controller 401 further includes an error signal monitorcomponent 420 configured to monitor and store error signals (e.g. errordata) caused by the navigation module 340 navigating the apparatus 103along a path through the environment. The error signals (and/orassociated error data) are stored in data 398 in association withrespective segments at which the error signals were acquired.

The learning controller 401 further includes an obstacle avoidercomponent 421 configured to avoid obstacles while the apparatus 103 isnavigating a path through the environment, for example based on datareceived from the one or more obstacle avoidance sensors 230; theobstacle avoider 421 one or more of: utilizes alternative existing pathsthrough the environment to avoid the obstacles; and generates new pathsthrough the environment to avoid the obstacles.

The learning controller 401 further includes a control input and errorsignal updater component 422 configured to update the control inputs andthe error signals stored in memory 322 based, at least in part, oncurrent control inputs and current error signals while the apparatus 103is navigating a path through the environment. For example, the controlinput and error signal updater 422 generates current control inputs fora given segment based on current error signals and the control inputs,as well as past error signals stored in the memory 322 for the givensegment; and stores the current control inputs and the current errorsignals in the memory 322 in association with the given segment.Furthermore, error signals for more than one previous navigation eventfor a given segment may be stored in the memory 322, and current controlinputs may be generated on a weighted average, and the like of two ormore previous set of error signals, with a higher weight being assignedto the most recent error signals.

Hence, the controller 32 is generally configured to: determine controlinputs and error signals for the navigation module 340 using thelearning controller 401; and update the control inputs and the errorsignals, based at least in part on the current control inputs and theresultant error signals, as well as past error signals, using thelearning controller 401.

Attention is now directed to FIG. 5 which depicts a flowchartrepresentative of an example method 500 of navigation of a mobileautomation apparatus using fixed segmented lattice planning. The exampleoperations of the method 500 of FIG. 5 correspond to machine readableinstructions that are executed by, for example, the apparatus 103, andspecifically by the controller 320 and/or the various components of thecontrol application 328 including, but not limited to, the learningcontroller 401. Indeed, the example method 500 of FIG. 5 is one way inwhich the apparatus 103 is configured. However, the following discussionof the example method 500 of FIG. 5 will lead to a further understandingof the apparatus 103, and its various components. However, it is to beunderstood that in other embodiments, the apparatus 103 and/or themethod 500 are varied, and hence need not work exactly as discussedherein in conjunction with each other, and that such variations arewithin the scope of present embodiments.

Furthermore, the example method 500 of FIG. 5 need not be performed inthe exact sequence as shown and likewise, in other embodiments, variousblocks may be performed in parallel rather than in sequence.Accordingly, the elements of method 500 are referred to herein as“blocks” rather than “steps.” The example method 500 of FIG. 5 may beimplemented on variations of the example apparatus 103, as well.

At block 501, the controller 320 generates a path using a subset of theplurality of segments stored in the data 398. For example, the block 501occurs using the path determiner 418. The block 501 occurs, in someimplementations, when a destination is received from the server 101, thepath that is generated being to the destination location. Alternatively,the block 501 is generated, not to a destination location, but pastgiven modules 110 in the environment, the path including, but notlimited to, a closed loop path (e.g. a path which loops back around onitself).

At block 503, as navigation occurs a first time along each of respectivesegments, using the navigation module 340, the controller 320 stores, inthe memory 322, control inputs and error signals for the navigationmodule 340 in association with each of the respective segments. Forexample, the block 503 occurs using the control input monitor 419 andthe error signal monitor 420.

At block 505, as navigation occurs at least a second time along a givensegment the controller 320: generates current control inputs for thegiven segment based on current error signals and the control inputs andthe error signals stored in the memory 322 for the given segment; andstores the current control inputs and the current error signals in thememory 322 in association with the given segment. For example, the block505 occurs using the control input and error signal updater 422.

At block 507, the controller 320 generates branches at each of aplurality of fixed nodes in the path, at least a portion of the branchescomprising a branch from a fixed node in the path to a respective fixednode in an adjacent path used to avoid obstacles in the path. Forexample, the block 507 occurs using the path determiner 418. The block507 occurs, in some implementations, when the path is generated, forexample in conjunction with the block 503.

At block 509, as navigation occurs along the path using the controlinputs and the error signals stored in the memory 322 in associationwith each of the respective segments of the first path, and when anobstacle is detected in the path, using the one or more obstacleavoidance sensors 230, the controller 320: controls the navigationmodule 340 to navigate from a fixed node in the path to a respectivefixed node in a second path to avoid the obstacle, using a branch fromthe fixed node in the path to the respective fixed node in the secondpath. For example, the second path can be an adjacent path, a parallelpath, a path within a constraint boundary, and the like. Indeed,navigation around an obstacle using a second path (as well as generationof the second path) is explained in further detail below with respect toFIG. 9 and FIG. 10. However, the second path generally comprises anypath that comprises the segments stored in the memory 322.

Also at the block 509 the controller 320 stores, in the memory 322 (e.g.in the data 398), the control inputs and the error signals for thenavigation module 340 in association with each of the respectivesegments for the second path. For example, the block 509 occurs usingthe control input monitor 419, the error signal monitor 420, theobstacle avoider 421 and, the control input and error signal updater422.

At block 511, when the one or more obstacle avoidance sensors 230indicate that the mobile automation apparatus 103 is past the obstacle,the controller 320: controls the navigation module 340 to navigate froma further respective fixed node in the second path to a further fixednode in the path, using another branch from the further respective fixednode in the second path to the further fixed node in the path; andstores, in the memory 322 (e.g. in the data 398), the control inputs andthe error signals for the navigation module in association with each ofthe respective segments for the second path and the path. For example,as will be explained below with reference to FIG. 10, when an obstacleis encountered along a path, for example an optimal path, the apparatus103 navigates to a second path to avoid the obstacle and, when past theobstacle, the apparatus 103 navigates back to the optimal path. In eachinstance, as the apparatus 103 navigates between the paths, navigationoccurs along segments that extend between fixed nodes in each path.Indeed, in general, in the present specification, the apparatus 103 isnavigating from fixed node to fixed node in the lattice configuration,and/or along segments between the fixed nodes

The method 500 is next described with reference to FIG. 6 to FIG. 10,each of which are substantially similar with like elements having likenumbers.

In particular, FIG. 6 depicts a top view of the apparatus 103 and amodule 110 as well as a plurality of segments 601 that can be used tonavigate through the environment of the modules 110 for example byforming a path 603 from the segments 601. As depicted, four exampleparallel paths 603 have been formed from the segments 601. For example,the path 603 closest to the module 110 can comprise an optimal path ofthe apparatus 103 for acquiring images of products, and the like, on theshelves of the module 110 at a given distance, while the remaining paths603 are alternate paths that the apparatus 103 can use to avoidobstacles, as described in more detail below.

Adjacent segments 601 are joined by fixed nodes 605. While the depictedexample fixed nodes 605 are shown as joining adjacent segments on eachof the example paths 603, the fixed nodes 605 can join other segments601, for example segments 601 on adjacent paths 603. Put another way,each of the segments 601 in FIG. 6, and as described elsewhere in thepresent specification, extend between fixed nodes 605.

Furthermore, the segments 601 and the fixed nodes 605 of the pluralityof paths 603 are arranged in a lattice configuration 604. The latticeconfiguration 604 generally comprises the fixed nodes 605 arranged in aperiodic pattern in at least two orthogonal directions (e.g., alongmodule 110 and perpendicular to the module 110 as shown in FIG. 6)according to a given resolution that sets a predetermined distance orsegment length between adjacent nodes 605 along the path and/or amongadjacent paths. For example, as shown in FIG. 6, the fixed nodes 605 arearranged in a grid pattern, such that the fixed nodes 605 are arrangedperiodically in two orthogonal directions. Furthermore, while theresolution of the lattice configuration 604 is the same for all thedepicted fixed nodes 605, the resolution of the lattice configurationcan be different for other regions and corresponding path segments ofthe environment (e.g., based on control input modification).Additionally, as those of skill in the art will realize, other periodicconfiguration patterns or grids of nodes 605 and segments 601, forexample a hexagonal pattern of nodes and segments, are within the scopeof embodiments of the present disclosure.

The resolution is generally dependent on operational constraints of theapparatus 103; for example, when the apparatus 103 is to navigateaccording to a given minimum speed, and/or within the constraint box609, and/or at given angles to the module 110, and the like, the fixednodes 605 in the lattice configuration are selected to be at aresolution that enables the apparatus 103 to maintain the speed and thepositional constraints and/or the angular constraints such that, atleast during obstacle avoidance the apparatus 103 operates within theconstraints. Furthermore in regions where the apparatus 103 is morelikely to encounter obstacles, the resolution can be higher than inother regions.

For example, the lattice configuration can have a first resolution in afirst region of the environment and a second resolution in a secondregion of the environment. In such examples, the lattice configurationcan have a first resolution (e.g. fixed nodes 605 per linear distance intwo directions) in a region of the environment adjacent to the module110 and/or within a constraint box 609 (described in further detailbelow) and/or in a region where data associated with the modules 110 isbeing acquired (e.g. images of products on shelves of the modules 110,and the like). The lattice configuration can have a second resolution ina region of the environment not adjacent to the module 110 and/oroutside of the constraint box 609 and/or in a region where dataassociated with the modules 110 is not being acquired.

Furthermore, in some implementations, wherein a resolution of thelattice configuration is dynamic, the controller 320 is furtherconfigured to: change a resolution of the lattice configuration in aregion to an updated resolution, and generate new control inputs anddetermine associated new error signals for each of updated segments inthe lattice configuration at the updated resolution. In other words,method 500 is repeated for new segments and/or fixed nodes when thelattice configuration resolution is changed, though the data associatedwith segments and/or fixed nodes in the previous resolution may continueto be stored in the event that the resolution of the latticeconfiguration changes back to the previous resolution.

Furthermore, positions of the segments 601 and the fixed nodes 605 arefurther fixed in a reference frame, for example a local coordinatereference frame, local to the environment of the modules 110 and/orlocal to one of the modules 110. As depicted, the reference frame has anorigin coordinate (0,0) at one of the fixed nodes 605. In someimplementations, the origin coordinate (and/or another location in theenvironment) coincides with a location in a global reference frame (e.g.as determined using a Global Positioning System device), and theapparatus 103 can be localized in the local reference and/or the globalreference frame.

In general, the positions of the segments 601 and the fixed nodes 605are defined by the data 398. While positions of the segments 601 and thefixed nodes 605 are depicted in FIG. 6, segments 601 and the fixed nodes605 are not physically marked in the environment of the modules 110(i.e., there are no painted lines and the like). Instead, thecoordinates corresponding to the positions of each of the segments 601(e.g., positions of two fixed nodes 605 between which any given segment601 extends), as well as coordinates corresponding to the positions ofeach of the fixed nodes 605 within the local coordinate reference frameare stored in the data 398 and used by the apparatus 103 for navigation.

FIG. 6 further depicts a “constraint box” 609 within which the segments601 and the fixed nodes 605 are located. The constraint box 609represents a region adjacent the module 110, within which the apparatus103 is to navigate to meet target data acquisition parameters, forexample a range of lateral distances from the module 110 where apparatus103 is to navigate to enable the data acquisition sensors of theapparatus 103 to reliably acquire data. For example, within theconstraint box 609, the data acquisition sensors of the apparatus 103reliably acquire images, and the like, of the shelves of the module 110and/or products and/or labels on the shelves. However, outside of theconstraint box 609, the data acquisition sensors of the apparatus 103may not reliably acquire images, and the like, of the shelves of themodule 110 and/or products and/or labels on the shelves. When the paths603 are generated, each of the paths 603 are located inside theconstraint box 609; hence, the constraint box 609 can be a given inputused to determine the paths 603 to ensure that along all the paths 603the apparatus 103 can acquire images of products and the like on theshelves of the module 110.

FIG. 6 further depicts an obstacle 699 for at least one of the paths603. The obstacle 699 includes, but is not limited to, displays (e.g. aretail display), pallets, shopping carts, people, and/or any objectwhich the apparatus 103 could collide with when navigating adjacent themodule 110 within the constraint box 609.

However, initially, the apparatus 103 has not detected the obstacle 699and a path through the constraint box 609 is generated without referenceto a position of the obstacle 699.

For example, with reference to FIG. 7, which depicts a non-limitingimplementation of the block 501 of the method 500, a path 703 (e.g. oneof the paths 603) is generated, for example by the controller 320, usinga subset of the plurality of segments 601. The path 703 represents, insome implementations, an optimal path for acquiring images, and thelike, of the module 110 using the data acquisition sensors.

In FIG. 7, The segments 601 of the path 703 are distinguished from theother segments 601 by the associated fixed nodes 605 being black, whilethe other fixed nodes 605 are white. The path 703 is generated, forexample, when the apparatus 103 receives a destination location, and thelike, for example from the server 101; in FIG. 7, it is assumed that thedestination location results in the controller 320 generating the path703.

Furthermore, the positions of the segments 601 and the fixed nodes 605do not change when generating the path 703. In other words, thegenerated path 703 for navigating past the module 110 is generated fromthe positions of the segments 601 and the fixed nodes 605; hence, evenwhen a path used to navigate past the module 110 changes, any updatedpath is generated from the segments 601 and the fixed nodes 605.

Hence, the apparatus 103 attempts to navigate the path 703. However, anyactual path navigated by the apparatus 103 can deviate from a generatedpath, for example when the generated path includes turns, sharp turns,variations in the floor 213 where the generated path is located, and thelike.

For each of the segments 601 in the path 703, the controller 320 stores(e.g. at the block 503), in the memory 322, control inputs used tocontrol the navigation module 340 in association with the segments 601,for example in the data 398. Similarly, for each of the segments 601 inthe path 703, the controller 120 stores (also at the block 503), in thememory 322, error signals generated as the segments 601 in the path 703are navigated. The error signals stored in association with the segments601, for example in the data 398.

Indeed, the control inputs stored in the memory 322 represent a bestattempt by the apparatus 103 to navigate each segment 601 of a generatedpath, with the error signals indicating deviations from the generatedpath at each segment 601 and/or navigation issues encountered duringnavigation at each segment 601.

For example, TABLE 1 provides a non-limiting example of the block 503(e.g. in which as navigation occurs a first time along each ofrespective segments 601, using the navigation module 340, and thecontroller 320 stores, in the memory 322, control inputs and errorsignals for the navigation module 340 in association with each of therespective segments). In particular, with reference to FIG. 7, each ofthe paths 603 are numbered Path 1 (e.g. path 703), Path 2, Path 3, Path4, and the first three parallel segments 601 in each of the paths 603are numbered as 1, 2, 3. As the apparatus 103 navigates path 703 (e.g.Path 1), the control inputs used to navigate the segments 601 are storedin the data 398, along with the resulting error signals.

TABLE 1 Segment Path 1 (703) Path 2 Path 3 Path 4 1 CI 1-1 DefaultDefault Default ES 1-1 {0} {0} {0} 2 CI 1-2 Default Default Default ES1-2 {0} {0} {0} 3 . . . CI 1-3 Default Default Default ES 1-3 {0} {0}{0} N CI 1-N Default Default Default ES 1-N {0} {0} {0}

Table 1, hence represents the control inputs (“CI”) 1-1, 1-2, 1-3 . . .1-N stored in the data 398 for each of “N” segments in the path 703(including the first three segments), along with accompanying errorsignals (“ES”) 1-1, 1-2, 1-3 . . . 1-N. For example, each of the controlinputs 1-1, 1-2, 1-3 . . . 1-N comprise the control data, and the like,used by the navigation module 340 to control the wheels 212, and thelike, along each of the “N” segments 601 in the path 703. Each of theerror signals 1-1, 1-2, 1-3 . . . 1-N comprise the resulting error data,and the like, that resulted when the corresponding the control inputs1-1, 1-2, 1-3 . . . 1-N were used by the navigation module 340 tocontrol the wheels 212, and the like, along each of the “N” segments 601in the path 703. An example of error data included in the error signalsincludes an obstacle indication associated with a given path segment,wheel slippage events, angle constraint violations, as well asnavigation instructions and/or actual navigation positions that resultedin constraint box 609 violations. In other words, the actual positionsof the apparatus 103 as the apparatus 103 is navigating a segment can bestored as part of the error data.

While not depicted, the TABLE 1 includes, in some implementations,positions of the segments 601 and/or the fixed nodes 605 in theenvironment; hence, the TABLE 1 represents a subset of the data 398.

As no segments 601 along the other paths have yet been navigated, thecontrol inputs comprise default control inputs (“Default”) and errorssignals each comprise a null set ({0}). The default control inputs areprovided, for example, by a manufacturer of the navigation module 340,and the like, and/or provisioned at a factory or during initial setup ina particular environment.

When the apparatus 103 navigates any segment 601 a second time, thecontrol inputs and error signals stored in the memory 322 for that givensegment are used to generate current control inputs, the current controlinputs also being based on current error signals. In other words, thecontroller 320 attempts to improve navigation along the given segment byupdating and/or refining the control input previously stored to reducethe error signals against the previously stored error signals when againnavigating the given segment. The current control inputs and currenterror signals are again stored in the memory 322.

Furthermore, in some implementations, error signals and correspondingcontrol inputs for several previous navigations of the given segment arestored and the current control input may further be based on a weightingof two or more stored error signals, including, but not limited to, aweighted average of the error signals, with greater weight beingassigned to more recent error signals. In some implementations, thecurrent control input may further be based on a weighted running averageof, for example, the last three error signals (and/or another number ofpast error signals) for the given segment.

As shown in TABLE 2, when the apparatus 103 navigates the path 703 atleast a second time, the controller 120 updates (e.g. at the block 505)the control inputs and the error signals stored in the memory 322, inassociation with each of the respective segments 601, with currentcontrol inputs and current error signals, for example to attempt toimprove navigation along each of the segments 601.

TABLE 2 Segment Path 1 (703) Path 2 Path 3 Path 4 1 CI-a 1-1 CI 2-1 CI3-1 CI 4-1 ES-a 1-1 ES 2-1 ES 3-1 ES 4-1 2 CI-a 1-2 CI 2-2 CI 3-2 CI 4-2ES-a 1-2 ES 2-2 ES 3-2 ES 4-2 3 . . . CI-a 1-3 CI 2-3 CI 3-3 CI 4-3 ES-a1-3 ES 2-3 ES 3-3 ES 4-3 N CI-a 1-N CI 2-N CI 3-N CI 4-N ES-a 1-N ES 2-NES 3-N ES 4-N

For example, with reference to TABLE 2, for each of the segments 601along the path 703 (e.g. Path 1), each of the control inputs and errorsignals are updated and/or refined as control inputs-a 1-1, 1-2, 1-3 . .. 1-N and error signals-a 1-1, 1-2, 1-3 . . . 1-N. For example, if theerror signal 1-1 indicates that the wheels 212 had slippage in thecorresponding segment 601, when the control input 1-1 was used by thenavigation module 340 to control the wheels 212, the second time theapparatus 103 navigates the same segment 601, the control input 1-1 isupdated and/or refined to the control input-a 1-1 which is generated bythe controller 320 to reduce and/or eliminate the slippage. Theresulting error signal-a 1-1 provides an indication of whether theslippage was reduced and/or eliminated and can further be monitored tofurther update current control inputs to further reduce and/or eliminateslippage during navigation of the segment.

Furthermore, each of the segments in TABLE 2 for each of the other paths(Path 1, Path 2, Path 3, Path 4) also show respective control inputs anderror signals, assuming that those segments have also been navigated atleast once. For example, the control inputs (“CI”) 2-1, 2-2, 2-3 . . .2-N and error signals (“ES”) 2-1, 2-2, 2-3 . . . 2-N are stored for afirst navigation for each segment in Path 2, the control inputs (“CI”)3-1, 3-2, 3-3 . . . 3-N and error signals (“ES”) 3-1, 3-2, 3-3 . . . 3-Nare stored for a first navigation for each segment in Path 3, and thecontrol inputs (“CI”) 4-1, 4-2, 4-3 . . . 4-N and error signals (“ES”)4-1, 4-2, 4-3 . . . 4-N are stored for a first navigation for eachsegment in Path 4.

In other words, each time the apparatus 103 navigates a given segment601, the corresponding stored control inputs and error signals, storedin association with the given segment 601, are used to generate currentcontrol inputs, along with current error signals, and the currentcontrol inputs and current error signals are stored in association withthe given segment. In this manner, the apparatus 103 navigates anenvironment on a segment-by-segment basis and further updates and/orrefines navigation through the environment on a segment-by-segmentbasis.

In the illustrated example, as the path 703 intersects the obstacle 699,as the apparatus 103 navigates along the path 703, the one or moreobstacle avoidance sensors 230 detect the obstacle 699.

Hence, with reference to FIG. 8, which depicts only path 703 adjacent tothe module 110 and the obstacle 699, at the block 507, the controller320 generates branches 801 at each of the plurality of fixed nodes 605in the path 703, at least a portion of the branches 801 comprising abranch from a fixed node 605 in the path 703 to a respective fixed nodein an adjacent path used to avoid obstacles in the path 703. In someimplementations, the branches 801 are generated when the path 703 isgenerated, in other implementations, the branches 801 are generated whenthe lattice of segments 601 and/or fixed nodes 605 are generated, whilein further implementations, the branches 801 are generated when theobstacle 699 is detected.

Furthermore, the branches 801 are generated at fixed nodes 605 on pathsother than the path along which the apparatus 103 is navigating. Thebranches 801 are generated between fixed nodes 605 according to anysuitable technique including, but not limited to, cubic splinetechniques, optimal trajectory generators, and the like; such techniquescan include, as inputs, operational constraints of the apparatus 103including, but not limited to, a minimum speed of the apparatus 103,turning constraints, angular constraints, and the like.

For example, attention is next directed to FIG. 9 which depictsschematically branches 901 (e.g. depicted within the area 902) from aplurality of the fixed nodes, represented by fixed nodes 905-1, 905-2,905-3, 905-4 (referred to hereafter collectively as a fixed node 905).Each of the branches 901 are from a fixed node on a lattice and is apotential alternative path segment available to the apparatus 103.

Furthermore, each of the branches 901 represent a further segment alongwhich the apparatus 103 may navigate, for example to continue on a givenpath and/or to avoid obstacles, for example a segment between fixednodes 605 on adjacent paths 603. Such segments represented by thebranches 901 are, in some implementations, perpendicular to the paths603 and/or at an angle to the paths 603, and/or are curved to accountfor a minimum speed of the apparatus, a turning radius, and the like, ofthe apparatus 103 at that speed, angular constraints of the apparatus103 when navigating relative to the module 110, and/or any otheroperational constraints.

Put another way, a respective set of default control inputs isassociated with each branch 901 that define an associated set of controlmotions executable by the apparatus 103 and/or the navigation module340.

It is further understood from FIG. 9 that for at least some of the fixednodes, respective branches 901 extend along a path that the apparatus103 is already navigating. For example, from the fixed nodes 905-1, abranch 901 extends to the fixed node 905-5. Indeed, for at least some ofthe fixed nodes, the branches 901 extend (e.g. from a direction ofnavigation of the apparatus 103 and/or a potential direction ofnavigation of the apparatus 103) “forward”, “backward”, “left” and“right”, with the branches 901 each defining at least a portion of asegment 601 negotiable by the apparatus 103. In other words, a shape ofa branch 901 is generated according to, for example, minimum speed ofthe apparatus, a turning radius, and the like, of the apparatus 103 atthat speed, angular constraints of the apparatus 103 when navigatingrelative to the module 110, and/or any other operational constraints.

As depicted, the branches 801, 901 are generated when the path 703 isgenerated, for example for the path 703, as in FIG. 8, as well as foradjacent paths 603, and/or for the fixed nodes 605 and/or the segments601 that could be included on adjacent paths 603.

Also depicted in FIG. 9 is an updated path 913 around the obstacle 699which includes portions of the path 703, as well as a portion of anotherof the paths, for example a second path 923 (e.g. another of the paths603), as described hereafter.

In particular, at least a portion of the branches 901 comprises a branchfrom a fixed node in a path to a respective fixed node in an adjacentpath used to avoid obstacles in the path.

For example, the branch 901 joining the fixed nodes 905-1, 905-2 is fromthe fixed node 905-1 in the path 703 to the fixed node 905-2 in thesecond path 923. Similarly, the branch 901 joining the fixed nodes905-3, 905-4 is from the fixed node 905-3 in the second path 923 to thefixed node 905-4 in the path 703. Furthermore, each of the branches 901between the fixed nodes 905 represent an additional segment 601 alongwhich the apparatus 103 navigates and for which the apparatus 103 storescontrol input and error signals.

In addition, generation of the branches 901 can occur only within thebounds of the constraint box 609; in other words, the branches 901 aregenerally generated only to fixed nodes that lie within the constraintbox 609.

In any event, when the apparatus 103 is navigating the path 703 and theobstacle 699 is detected using the one or more obstacle avoidancesensors 230, the controller 320 (e.g. at the block 509): controls thenavigation module 340 to navigate from the fixed node 905-1 in the path703 to the respective fixed node 905-2 in the second path 923 to avoidthe obstacle 699, using a branch 801 from the fixed node 905-1 in thepath 703 to the respective fixed node 905-2 in the second path 923.

Similarly, when the one or more obstacle avoidance sensors 230 indicatethat the mobile automation apparatus 103 is past the obstacle 699, thecontroller 320 (e.g. at the block 511): controls the navigation module340 to navigate from the fixed node 905-3 in the second path 923 to therespective fixed node 905-4 in the second path 703 (e.g. returning to anoptimum path), using a branch 801 from the fixed node 905-3 in thesecond path 923 to the respective fixed node 905-4 in the path 703.

Furthermore, as each branch 801 is generated, each of the branches 801is associated with a set of respective control inputs used by thenavigation module 340 to navigate from a respective fixed node in thepath to one or more fixed nodes in adjacent paths.

For example, the set of respective control inputs for each branch 801includes, but is not limited to, control inputs for turning and/orsteering and/or braking the wheels 212 to navigate between the fixednodes 905 between which a given branch 801 extends, and the like.

Attention is next directed to FIG. 10 which depicts the path 913 withoutdepiction of all of the branches 801, as well as the paths 703, 923whose portions form the path 913. The fixed nodes of each of the paths703, 923 which form the path 913 are depicted using black circles, whilethe remaining fixed nodes are depicted using white circles.

As in FIG. 9, the path 913 includes a branch and/or segment between thefixed nodes 905-1, 905-2, and a branch and/or segment between the fixednodes 905-3, 905-4. However, also depicted in FIG. 10 is an actual path1005 between the fixed nodes 905-1, 905-2, and an actual path 1006between the fixed nodes 905-3, 905-4. In other words, the apparatus 103attempts to navigate (e.g. at the block 509) between the fixed nodes905-1, 905-2 to avoid the obstacle 699 (e.g. from the path 703 to thesecond path 923), and when the apparatus 103 is past the obstacle 699,the apparatus 103 attempts to navigate (e.g. at the block 511) betweenthe fixed nodes 905-3, 905-4 (e.g. from the second path 923 back to thepath 703).

In other words, the path 1005 represents a best first attempt by theapparatus 103 to navigate the branches and/or the segments between thefixed nodes 905. However, the path 1005 does not exactly follow thebranches and/or the segments between the fixed nodes 905. Indeed, at aposition 1001, the apparatus 103 is outside of the constraint box 609.Furthermore, at a position 1003, the apparatus 103 violates an angleconstraint.

Hence, for each of the corresponding segments of the path 913, theapparatus 103 further stores the control inputs and the error signals inassociation therewith. For example, for the segments of the path 913that correspond to the actual path 1005, error signals are stored, theerror signals representing one or more of: deviations from the path 913,violations of location constraints (e.g. being outside the constraintbox 609, violations of angle constraints, and the like.

Hence, the next time the apparatus 103 navigates the path 913, theapparatus 103 attempts to reduce the error signals and align the actualpath 1005 with the path 913, hence refining the control inputs and theerror signals.

Put another way, in some implementations the controller 320: determineswhen one or more constraints for navigating the given segment has beenviolated; and generate the current control inputs that keep the one ormore constraints within respective threshold values, for example forkeeping the apparatus 103 within a given threshold distance from atarget path, keeping the apparatus 103 within a given threshold anglefrom the module 110, and the like.

Put yet another way, the controller 320 is configured to cause theapparatus 103 to change from navigating along a first path to navigatingalong a second path when one or more constraints for navigating therespective segments 601 have been violated, for example by navigatingalong an updated path (e.g. the second path) that meets the one or moreconstraints. It is appreciated, however, that the updated path is alongexisting segments 601 and/or along existing fixed nodes 605.

Furthermore, the next time the apparatus 103 detects the obstacle 699,the apparatus 103 again navigates according to the segments of the path913 as the branches 901 define the possible segments along which theapparatus 103 can navigate when deviating from the initially generatedpath 703. However, the apparatus 103 performs navigational learning ateach segment 601 and/or branch and/or fixed node 605 in the lattice,using the learning controller 401. Hence, as the apparatus 103 navigatesin the environment, the apparatus 103 gradually improves the navigationon a segment-by-segment basis by refining navigation control inputsbased on past error signals and/or current error signals.

Indeed, again referring to TABLE 1 and TABLE 2, when the apparatus 103navigates various segments of Path 1, Path 2, Path 3 and Path 4 at leasta second time, the controller 120 updates (e.g. at the block 505) thecontrol inputs and the error signals stored in the memory 322, inassociation with each of the respective segments 601, with currentcontrol inputs and current error signals, for example to attempt toimprove navigation along each of the segments 601. For example, in TABLE3, it assumed that an obstacle was detected in Path 1, and the apparatus103 navigated to Path 4 to avoid the obstacle, and then back to Path 1.

TABLE 3 Segment Path 1 (703) Path 2 Path 3 Path 4 1 CI-b 1-1 CI 2-1 CI3-1 CI 4-1 ES-b1-1 ES 2-1 ES 3-1 ES 4-1 2 CI-a 1-2 CI 2-2 CI 3-2 CI-a4-2 ES-a 1-2 ES 2-2 ES 3-2 ES-a 4-2 3 . . . CI-a 1-3 CI 2-3 CI 3-3 CI-a4-3 ES-a 1-3 ES 2-3 ES 3-3 ES-a 4-3 N CI-b 1-N CI 2-N CI 3-N CI 4-N ES-b1-N ES 2-N ES 3-N ES 4-N

In particular, segment 1 and segment N of Path 1 were navigated threetimes previously (e.g. two previous times, similar to TABLE 2, and thena third time before and after the obstacle). For example, the letter “b”in the control input CI-b 1-1, and CI-b 1-N (and in the error signalsES-b 1-1, and ES-b 1-N) indicates that the segments were navigated threetimes.

Similarly, segment 2 and segment 3 of Path 4 were navigated twice (aprevious time, similar to TABLE 2, and then a second time as theapparatus 103 navigated to those segments to avoid the obstacle). Forexample, the letter “a” in the control input CI-a 4-2, and CI-a 4-3 (andin the error signals ES-a 4-2, and ES-a 4-3) indicates that the segmentswere navigated two times.

While not depicted, TABLE 3 can also store control inputs and errorsignals for segments and/or branches used to navigate between Path 1 andPath 2.

Attention is now directed to FIG. 11 which depicts a flowchartrepresentative of an example method 1100 of navigation of a mobileautomation apparatus using fixed segmented lattice planning. The exampleoperations of the method 1100 of FIG. 11 correspond to machine readableinstructions that are executed by, for example, the apparatus 103, andspecifically by the controller 320 and/or the various components of thecontrol application 328 including, but not limited to, the learningcontroller 401. Indeed, the example method 1100 of FIG. 11 is one way inwhich the apparatus 103 is configured. However, the following discussionof the example method 1100 of FIG. 11 will lead to a furtherunderstanding of the apparatus 103, and its various components. However,it is to be understood that in other embodiments, the apparatus 103and/or the method 1100 are varied, and hence need not work exactly asdiscussed herein in conjunction with each other, and that suchvariations are within the scope of present embodiments.

Furthermore, the example method 1100 of FIG. 11 need not be performed inthe exact sequence as shown and likewise, in other embodiments, variousblocks may be performed in parallel rather than in sequence.Accordingly, the elements of method 1100 are referred to herein as“blocks” rather than “steps.” The example method 1100 of FIG. 11 may beimplemented on variations of the example apparatus 103, as well.

It is further assumed in the method 1100 that the data 398 has beenprovisioned at the memory 322 and that the apparatus 103 is at, oradjacent to, one of the fixed nodes 605 defined in the data 398.

At block 1101, the controller 320 receives a destination location, forexample from the server 101 and/or mobile device 105.

At block 1103, the controller 320 determines which branches 901 areavailable to navigate, for example from the given fixed node 605 wherethe apparatus 103 is currently located. Alternatively, at the block1103, the controller 320 generates branches using the locations of thefixed nodes 605 stored in the data 398

At block 1105, the controller 320 selects a branch 901 to navigate and,at block 1107, the controller 320 determines whether there is anobstacle in the direction of the selected branch 901 using the one ormore obstacle avoidance sensors 230.

If so (e.g. a “YES” decision at the block 1107), the block 1105 isexecuted again until no obstacle is detected at the block 1107 (e.g. a“NO” decision at the block 1107), when, at block 1109, the controller320 loads a path to be navigated to the destination location and thatincludes the selected branch 901 where no obstacle is detected.

At the block 1111, the controller 320 loads previous control inputs anderror signals, if any, for navigating segments in the path loaded at theblock 1109, and at block 1113, the controller 320 controls thenavigation module 340 to navigate the path using the previous controlinputs, if any. If there are no previous control inputs, the controller320 navigates the path at least along a first segment 601 according tonavigation sensors and the like and/or using any default control inputs.Regardless, as the path is navigated, the controller 320 monitors theerror signals and/or navigates to reduce stored error signals. Hence, aseach segment 601 is navigated at least a second time, the apparatus 103iteratively learns to navigate each segment 601.

At block 1115, the controller 320 stores current control inputs andcurrent error signals for the segment 601 (and/or segments 601) that hasbeen navigated, for example as described above with regards to TABLE 1and TABLE 2.

The method 1100 repeats from the block 1103 at each of the fixed nodes605 and/or at every “nth” fixed node, and the like, until the apparatus103 arrives at the destination location, as determined at the block1113, where “n” is an integer that is configurable at the apparatus 103.The method 1100 either ends when the destination location is reached, asdetermined at the block 1113, or repeats when another destinationlocation is received (e.g. at a “NO” decision at the block 1113, block1103 is re-executed, and at “YES” decision at the block 1113, the method1100 ends or the block 1101 is re-executed).

Hence, as the blocks 1103 to 1113 of the method 1100 are executable ateach of the fixed nodes 605, in some implementations, the apparatus 103navigates between fixed nodes 605 arranged in a lattice configuration.Furthermore, in some implementations, the control inputs and errorsignals are stored in association with the fixed nodes and/or respectivesegments.

The present specification is directed to fixed segmented latticeplanning for a mobile automation apparatus and, for example, iterativelearning of paths through an environment on a segment-by-segment basis,the segments and fixed nodes associated with segments being fixed inlocation in a reference frame and arranged in a lattice configuration.Further, as the mobile automation apparatus navigates using differentsegments to avoid obstacles, control inputs are “learned” and/or refinedand/or updated for each segment in the lattice that is navigated.Indeed, at each fixed node in the lattice, the mobile automationapparatus can execute any of a plurality of a respective set controlmotions represented by branches from each of the fixed nodes, which willcause the mobile automation apparatus to navigate to a next fixed nodein the lattice. Each fixed node and its associated branches aregenerally fixed in a static reference frame, which can be local orglobal. Furthermore, for every branch of every lattice, the controlinputs and error signals are stored and then retrieved a next time themobile automation apparatus uses a branch, and which are used to improvepath tracking. Furthermore, as the environment changes, the apparatus103 adapts to the environment through continued obstacle detection, andrefining of the control inputs and error signals for any segments usedto avoid the obstacles. Environmental changes can also result in theapparatus 103 determining an updated optimal path through theenvironment, along with the segment-by-segment learning techniquesdescribed herein also occur.

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art appreciates thatvarious modifications and changes can be made without departing from thescope of the invention as set forth in the claims below. Accordingly,the specification and figures are to be regarded in an illustrativerather than a restrictive sense, and all such modifications are intendedto be included within the scope of present teachings.

The benefits, advantages, solutions to problems, and any element(s) thatmay cause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover, in this document, language of “at least one of X, Y, and Z”and “one or more of X, Y and Z” can be construed as X only, Y only, Zonly, or any combination of two or more items X, Y, and Z (e.g., XYZ,XY, YZ, XZ, and the like). Similar logic can be applied for two or moreitems in any occurrence of “at least one . . . ” and “one or more . . .” language.

Moreover, in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises . . . a”, “has . . . a”, “includes . . . a”, “contains . . .a” does not, without more constraints, preclude the existence ofadditional identical elements in the process, method, article, orapparatus that comprises, has, includes, contains the element. The terms“a” and “an” are defined as one or more unless explicitly statedotherwise herein. The terms “substantially”, “essentially”,“approximately”, “about” or any other version thereof, are defined asbeing close to as understood by one of ordinary skill in the art, and inone non-limiting embodiment the term is defined to be within 10%, inanother embodiment within 5%, in another embodiment within 1% and inanother embodiment within 0.5%. The term “coupled” as used herein isdefined as connected, although not necessarily directly and notnecessarily mechanically. A device or structure that is “configured” ina certain way is configured in at least that way, but may also beconfigured in ways that are not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, an embodiment can be implemented as a computer-readablestorage medium having computer readable code stored thereon forprogramming a computer (e.g., comprising a processor) to perform amethod as described and claimed herein. Examples of suchcomputer-readable storage mediums include, but are not limited to, ahard disk, a CD-ROM, an optical storage device, a magnetic storagedevice, a ROM (Read Only Memory), a PROM (Programmable Read OnlyMemory), an EPROM (Erasable Programmable Read Only Memory), an EEPROM(Electrically Erasable Programmable Read Only Memory) and a Flashmemory. Further, it is expected that one of ordinary skill,notwithstanding possibly significant effort and many design choicesmotivated by, for example, available time, current technology, andeconomic considerations, when guided by the concepts and principlesdisclosed herein will be readily capable of generating such softwareinstructions and programs and ICs with minimal experimentation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

We claim:
 1. A mobile automation apparatus comprising: a memory storinga plurality of segments for a plurality of paths through an environment,each of the plurality of segments being fixed in a reference frame, andextending between fixed nodes arranged in a lattice configuration; anavigation module having at least one motor configured to move themobile automation apparatus in the environment; and a navigationcontroller configured to: as navigation occurs a first time along eachof respective segments using the navigation module, store, in thememory, control inputs and error signals for the navigation module inassociation with each of the respective segments; and, as navigationoccurs at least a second time along a given segment: generate currentcontrol inputs for the given segment based on current error signals andthe control inputs and the error signals stored in the memory for thegiven segment; and store the current control inputs and the currenterror signals in the memory in association with the given segment. 2.The mobile automation apparatus of claim 1, further comprising one ormore obstacle avoidance sensors, wherein the navigation controller isfurther configured to: navigate along a first path using the controlinputs and the error signals stored in the memory in association witheach of the respective segments of the first path; when an obstacle isdetected in the first path, using the one or more obstacle avoidancesensors: control the navigation module to navigate from a fixed node inthe first path to a respective fixed node in a second path to avoid theobstacle, using a branch from the fixed node in the first path to therespective fixed node in the second path; and store, in the memory, thecontrol inputs and the error signals for the navigation module inassociation with each of the respective segments for the second path;and, when the one or more obstacle avoidance sensors indicate that themobile automation apparatus is past the obstacle: control the navigationmodule to navigate from a further respective fixed node in the secondpath to a further fixed node in the first path, using another branchfrom the further respective fixed node in the second path to the furtherfixed node in the first path; and store, in the memory, the controlinputs and the error signals for the navigation module in associationwith each of the respective segments for the second path and the firstpath.
 3. The mobile automation apparatus of claim 2, wherein the secondpath is one or more of: adjacent to the first path; about parallel tothe first path; and within a given constraint boundary.
 4. The mobileautomation apparatus of claim 1, further comprising a communicationinterface configured to receive a destination position in theenvironment, and wherein the navigation controller is further configuredto: generate a path to the destination position using a subset of theplurality of segments.
 5. The mobile automation apparatus of claim 4,wherein the navigation controller is further configured to: generatebranches at each fixed node in the path, at least a portion of thebranches comprising a branch from a fixed node in a first path to arespective fixed node in an adjacent path used to avoid obstacles in thepath.
 6. The mobile automation apparatus of claim 5, wherein thenavigation controller is further configured to: generate branches ateach fixed node in the path when the path is generated.
 7. The mobileautomation apparatus of claim 4, wherein the controller is furtherconfigured to: generate branches at each fixed node in the path, each ofthe branches associated with a set of respective control inputs used bythe navigation module to navigate from a respective fixed node in thepath to one or more fixed nodes in adjacent paths.
 8. The mobileautomation apparatus of claim 1, wherein the navigation controller isfurther configured to: determine the control inputs and the errorsignals for the navigation module using a learning controller; andgenerate the current control inputs using the learning controller. 9.The mobile automation apparatus of claim 1, wherein the navigationcontroller is further configured to: determine when one or moreconstraints for navigating the given segment has been violated; andgenerate the current control inputs that keep the one or moreconstraints within respective threshold values.
 10. The mobileautomation apparatus of claim 1, wherein the navigation controller isfurther configured to change a current path when one or more constraintsfor navigating the respective segments have been violated by navigatingalong an updated path that meets the one or more constraints.
 11. Themobile automation apparatus of claim 1, wherein the reference frame isassociated with the environment.
 12. The mobile automation apparatus ofclaim 1, wherein the lattice configuration comprises the fixed nodesarranged in a periodic pattern in at least two orthogonal directionsaccording to a given resolution.
 13. The mobile automation apparatus ofclaim 1, wherein the lattice configuration has a first resolution in afirst region of the environment and a second resolution in a secondregion of the environment.
 14. The mobile automation apparatus of claim1, wherein a resolution of the lattice configuration is dynamic, and thenavigation controller is further configured to: change a resolution ofthe lattice configuration in a region to an updated resolution, andgenerate new control inputs and determine associated new error signalsfor each of updated segments in the lattice configuration at the updatedresolution.
 15. A method comprising: at a mobile automation apparatuscomprising: a memory storing a plurality of segments for a plurality ofpaths through an environment, each of the plurality of segments beingfixed in a reference frame, and extending between fixed nodes arrangedin a lattice configuration; a navigation module having at least onemotor configured to move the mobile automation apparatus in theenvironment, as navigation occurs a first time along each of respectivesegments using the navigation module, storing, using the navigationcontroller, in the memory, control inputs and error signals for thenavigation module in association with each of the respective segments;and, as navigation occurs at least a second time along a given segment:generating, using the navigation controller, current control inputs forthe given segment based on current error signals and the control inputsand the error signals stored in the memory for the given segment; andstoring, using the navigation controller, the current control inputs andthe current error signals in the memory in association with the givensegment.
 16. The method of claim 15, wherein the mobile automationapparatus further comprises one or more obstacle avoidance sensors, andthe method further comprises: navigating, using the navigationcontroller, along a first path using the control inputs and the errorsignals stored in the memory in association with each of the respectivesegments of the first path; when an obstacle is detected in the firstpath, using the one or more obstacle avoidance sensors: controlling,using the navigation controller, the navigation module to navigate froma fixed node in the first path to a respective fixed node in a secondpath to avoid the obstacle, using a branch from the fixed node in thefirst path to the respective fixed node in the second path; and storing,using the navigation controller, in the memory, the control inputs andthe error signals for the navigation module in association with each ofthe respective segments for the second path; and, when the one or moreobstacle avoidance sensors indicate that the mobile automation apparatusis past the obstacle: controlling, using the navigation controller, thenavigation module to navigate from a further respective fixed node inthe second path to a further fixed node in the first path, using anotherbranch from the further respective fixed node in the second path to thefurther fixed node in the first path; and storing, using the navigationcontroller, in the memory, the control inputs and the error signals forthe navigation module in association with each of the respectivesegments for the second path and the first path.
 17. The method of claim15, wherein the mobile automation apparatus further comprises acommunication interface configured to receive a destination position inthe environment, and wherein the method further comprises generating,using the navigation controller, a path to the destination positionusing a subset of the plurality of segments.
 18. The method of claim 15,further comprising: determining the control inputs and the error signalsfor the navigation module using a learning controller; and generatingthe current control inputs using the learning controller.
 19. The methodof claim 15, further comprising: determining when one or moreconstraints for navigating the given segment has been violated; andgenerating the current control inputs that keep the one or moreconstraints within respective threshold values.
 20. A non-transitorycomputer-readable medium storing a computer program, wherein executionof the computer program is for: at a mobile automation apparatuscomprising: a memory storing a plurality of segments for a plurality ofpaths through an environment, each of the plurality of segments beingfixed in a reference frame, and extending between fixed nodes arrangedin a lattice configuration; a navigation module having at least onemotor configured to move the mobile automation apparatus in theenvironment, as navigation occurs a first time along each of respectivesegments using the navigation module, storing, using the navigationcontroller, in the memory, control inputs and error signals for thenavigation module in association with each of the respective segments;and, as navigation occurs at least a second time along a given segment:generating, using the navigation controller, current control inputs forthe given segment based on current error signals and the control inputsand the error signals stored in the memory for the given segment; andstoring, using the navigation controller, the current control inputs andthe current error signals in the memory in association with the givensegment.